diff --git a/mllib/src/main/scala/org/apache/spark/ml/evaluation/MulticlassClassificationEvaluator.scala b/mllib/src/main/scala/org/apache/spark/ml/evaluation/MulticlassClassificationEvaluator.scala
index 8408516751102..390e9b6444c74 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/evaluation/MulticlassClassificationEvaluator.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/evaluation/MulticlassClassificationEvaluator.scala
@@ -28,7 +28,7 @@ import org.apache.spark.sql.types.DoubleType
 
 /**
  * :: Experimental ::
- * Evaluator for multiclass classification, which expects two input columns: score and label.
+ * Evaluator for multiclass classification, which expects two input columns: prediction and label.
  */
 @Since("1.5.0")
 @Experimental
diff --git a/python/pyspark/ml/evaluation.py b/python/pyspark/ml/evaluation.py
index fc9099b7ec172..16029dc34863a 100644
--- a/python/pyspark/ml/evaluation.py
+++ b/python/pyspark/ml/evaluation.py
@@ -193,9 +193,6 @@ class RegressionEvaluator(JavaEvaluator, HasLabelCol, HasPredictionCol):
 
     .. versionadded:: 1.4.0
     """
-    # Because we will maximize evaluation value (ref: `CrossValidator`),
-    # when we evaluate a metric that is needed to minimize (e.g., `"rmse"`, `"mse"`, `"mae"`),
-    # we take and output the negative of this metric.
     metricName = Param(Params._dummy(), "metricName",
                        """metric name in evaluation - one of:
                        rmse - root mean squared error (default)
@@ -270,7 +267,7 @@ class MulticlassClassificationEvaluator(JavaEvaluator, HasLabelCol, HasPredictio
     """
     metricName = Param(Params._dummy(), "metricName",
                        "metric name in evaluation "
-                       "(f1|precision|recall|weightedPrecision|weightedRecall)",
+                       "(f1|precision|recall|weightedPrecision|weightedRecall|accuracy)",
                        typeConverter=TypeConverters.toString)
 
     @keyword_only