[SPARK-19590][pyspark][ML] update the document for QuantileDiscretizer in pyspark

This PR is to document the change on QuantileDiscretizer in pyspark for PR:
#15428

Signed-off-by: VinceShieh <[email protected]>

Author: VinceShieh

python/pyspark/ml/feature.py

@@ -1178,7 +1178,17 @@ class QuantileDiscretizer(JavaEstimator, HasInputCol, HasOutputCol, JavaMLReadab
 
     `QuantileDiscretizer` takes a column with continuous features and outputs a column with binned
     categorical features. The number of bins can be set using the :py:attr:`numBuckets` parameter.
-    The bin ranges are chosen using an approximate algorithm (see the documentation for
+    It is possible that the number of buckets used will be less than this value, for example, if
+    there are too few distinct values of the input to create enough distinct quantiles.
+
+    NaN handling: Note also that
+    QuantileDiscretizer will raise an error when it finds NaN values in the dataset, but the user
+    can also choose to either keep or remove NaN values within the dataset by setting
+    `handleInvalid`. If the user chooses to keep NaN values, they will be handled specially and
+    placed into their own bucket, for example, if 4 buckets are used, then non-NaN data will be
+    put into buckets[0-3], but NaNs will be counted in a special bucket[4].
+
+    Algorithm: The bin ranges are chosen using an approximate algorithm (see the documentation for
     :py:meth:`~.DataFrameStatFunctions.approxQuantile` for a detailed description).
     The precision of the approximation can be controlled with the
     :py:attr:`relativeError` parameter.