diff --git a/python/pyspark/ml/clustering.py b/python/pyspark/ml/clustering.py
index 64c4bf1b927d7ae1fcd86232d29caa3e5a31a956..05aa2dfe74b7efa3d78b9b7964677dfeb05d7262 100644
--- a/python/pyspark/ml/clustering.py
+++ b/python/pyspark/ml/clustering.py
@@ -92,7 +92,8 @@ class KMeans(JavaEstimator, HasFeaturesCol, HasPredictionCol, HasMaxIter, HasTol
     initMode = Param(Params._dummy(), "initMode",
                      "the initialization algorithm. This can be either \"random\" to " +
                      "choose random points as initial cluster centers, or \"k-means||\" " +
-                     "to use a parallel variant of k-means++", TypeConverters.toString)
+                     "to use a parallel variant of k-means++",
+                     typeConverter=TypeConverters.toString)
     initSteps = Param(Params._dummy(), "initSteps", "steps for k-means initialization mode",
                       typeConverter=TypeConverters.toInt)
 
diff --git a/python/pyspark/ml/feature.py b/python/pyspark/ml/feature.py
index 49a78ede37d17c5362f236185312a6918d37a412..4310f154b51860b917e87ed2f4e113d841c5502a 100644
--- a/python/pyspark/ml/feature.py
+++ b/python/pyspark/ml/feature.py
@@ -1317,9 +1317,9 @@ class RegexTokenizer(JavaTransformer, HasInputCol, HasOutputCol, JavaMLReadable,
                            typeConverter=TypeConverters.toInt)
     gaps = Param(Params._dummy(), "gaps", "whether regex splits on gaps (True) or matches tokens")
     pattern = Param(Params._dummy(), "pattern", "regex pattern (Java dialect) used for tokenizing",
-                    TypeConverters.toString)
+                    typeConverter=TypeConverters.toString)
     toLowercase = Param(Params._dummy(), "toLowercase", "whether to convert all characters to " +
-                        "lowercase before tokenizing", TypeConverters.toBoolean)
+                        "lowercase before tokenizing", typeConverter=TypeConverters.toBoolean)
 
     @keyword_only
     def __init__(self, minTokenLength=1, gaps=True, pattern="\\s+", inputCol=None,
@@ -1430,7 +1430,8 @@ class SQLTransformer(JavaTransformer, JavaMLReadable, JavaMLWritable):
     .. versionadded:: 1.6.0
     """
 
-    statement = Param(Params._dummy(), "statement", "SQL statement", TypeConverters.toString)
+    statement = Param(Params._dummy(), "statement", "SQL statement",
+                      typeConverter=TypeConverters.toString)
 
     @keyword_only
     def __init__(self, statement=None):
@@ -1504,9 +1505,10 @@ class StandardScaler(JavaEstimator, HasInputCol, HasOutputCol, JavaMLReadable, J
     .. versionadded:: 1.4.0
     """
 
-    withMean = Param(Params._dummy(), "withMean", "Center data with mean", TypeConverters.toBoolean)
+    withMean = Param(Params._dummy(), "withMean", "Center data with mean",
+                     typeConverter=TypeConverters.toBoolean)
     withStd = Param(Params._dummy(), "withStd", "Scale to unit standard deviation",
-                    TypeConverters.toBoolean)
+                    typeConverter=TypeConverters.toBoolean)
 
     @keyword_only
     def __init__(self, withMean=False, withStd=True, inputCol=None, outputCol=None):
@@ -1754,7 +1756,7 @@ class StopWordsRemover(JavaTransformer, HasInputCol, HasOutputCol, JavaMLReadabl
     stopWords = Param(Params._dummy(), "stopWords", "The words to be filtered out",
                       typeConverter=TypeConverters.toListString)
     caseSensitive = Param(Params._dummy(), "caseSensitive", "whether to do a case sensitive " +
-                          "comparison over the stop words", TypeConverters.toBoolean)
+                          "comparison over the stop words", typeConverter=TypeConverters.toBoolean)
 
     @keyword_only
     def __init__(self, inputCol=None, outputCol=None, stopWords=None,
@@ -2510,7 +2512,8 @@ class RFormula(JavaEstimator, HasFeaturesCol, HasLabelCol, JavaMLReadable, JavaM
     .. versionadded:: 1.5.0
     """
 
-    formula = Param(Params._dummy(), "formula", "R model formula", TypeConverters.toString)
+    formula = Param(Params._dummy(), "formula", "R model formula",
+                    typeConverter=TypeConverters.toString)
 
     @keyword_only
     def __init__(self, formula=None, featuresCol="features", labelCol="label"):
diff --git a/python/pyspark/ml/recommendation.py b/python/pyspark/ml/recommendation.py
index 9c38f2431b8d0681c427617b6b47d782a8d81620..9d7f22a66fde64b8b7c524dbe6224fe99998ed9a 100644
--- a/python/pyspark/ml/recommendation.py
+++ b/python/pyspark/ml/recommendation.py
@@ -107,16 +107,18 @@ class ALS(JavaEstimator, HasCheckpointInterval, HasMaxIter, HasPredictionCol, Ha
     numItemBlocks = Param(Params._dummy(), "numItemBlocks", "number of item blocks",
                           typeConverter=TypeConverters.toInt)
     implicitPrefs = Param(Params._dummy(), "implicitPrefs", "whether to use implicit preference",
-                          TypeConverters.toBoolean)
+                          typeConverter=TypeConverters.toBoolean)
     alpha = Param(Params._dummy(), "alpha", "alpha for implicit preference",
                   typeConverter=TypeConverters.toFloat)
-    userCol = Param(Params._dummy(), "userCol", "column name for user ids", TypeConverters.toString)
-    itemCol = Param(Params._dummy(), "itemCol", "column name for item ids", TypeConverters.toString)
+    userCol = Param(Params._dummy(), "userCol", "column name for user ids",
+                    typeConverter=TypeConverters.toString)
+    itemCol = Param(Params._dummy(), "itemCol", "column name for item ids",
+                    typeConverter=TypeConverters.toString)
     ratingCol = Param(Params._dummy(), "ratingCol", "column name for ratings",
-                      TypeConverters.toString)
+                      typeConverter=TypeConverters.toString)
     nonnegative = Param(Params._dummy(), "nonnegative",
                         "whether to use nonnegative constraint for least squares",
-                        TypeConverters.toBoolean)
+                        typeConverter=TypeConverters.toBoolean)
 
     @keyword_only
     def __init__(self, rank=10, maxIter=10, regParam=0.1, numUserBlocks=10, numItemBlocks=10,