diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/Bucketizer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/Bucketizer.scala
index f99649f7fa164..0b989b0d7d253 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/Bucketizer.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/Bucketizer.scala
@@ -89,7 +89,8 @@ final class Bucketizer @Since("1.4.0") (@Since("1.4.0") override val uid: String
   def setOutputCol(value: String): this.type = set(outputCol, value)
 
   /**
-   * Param for how to handle invalid entries. Options are 'skip' (filter out rows with
+   * Param for how to handle invalid entries containing NaN values. Values outside the splits
+   * will always be treated as errors. Options are 'skip' (filter out rows with
    * invalid values), 'error' (throw an error), or 'keep' (keep invalid values in a special
    * additional bucket). Note that in the multiple column case, the invalid handling is applied
    * to all columns. That said for 'error' it will throw an error if any invalids are found in
@@ -99,7 +100,8 @@ final class Bucketizer @Since("1.4.0") (@Since("1.4.0") override val uid: String
    */
   @Since("2.1.0")
   override val handleInvalid: Param[String] = new Param[String](this, "handleInvalid",
-    "how to handle invalid entries. Options are skip (filter out rows with invalid values), " +
+    "how to handle invalid entries containing NaN values. Values outside the splits will always " +
+    "be treated as errorsOptions are skip (filter out rows with invalid values), " +
     "error (throw an error), or keep (keep invalid values in a special additional bucket).",
     ParamValidators.inArray(Bucketizer.supportedHandleInvalids))
 
diff --git a/python/pyspark/ml/feature.py b/python/pyspark/ml/feature.py
index eccb7acae5b98..3d23700242594 100755
--- a/python/pyspark/ml/feature.py
+++ b/python/pyspark/ml/feature.py
@@ -361,8 +361,9 @@ class Bucketizer(JavaTransformer, HasInputCol, HasOutputCol, HasHandleInvalid,
               "splits specified will be treated as errors.",
               typeConverter=TypeConverters.toListFloat)
 
-    handleInvalid = Param(Params._dummy(), "handleInvalid", "how to handle invalid entries. " +
-                          "Options are 'skip' (filter out rows with invalid values), " +
+    handleInvalid = Param(Params._dummy(), "handleInvalid", "how to handle invalid entries "
+                          "containing NaN values. Values outside the splits will always be treated "
+                          "as errors. Options are 'skip' (filter out rows with invalid values), " +
                           "'error' (throw an error), or 'keep' (keep invalid values in a special " +
                           "additional bucket).",
                           typeConverter=TypeConverters.toString)