init pr

Author: WeichenXu123

docs/ml-tuning.md

@@ -55,7 +55,7 @@ for multiclass problems. The default metric used to choose the best `ParamMap` c
 method in each of these evaluators.
 
 To help construct the parameter grid, users can use the [`ParamGridBuilder`](api/scala/index.html#org.apache.spark.ml.tuning.ParamGridBuilder) utility.
-By default, sets of parameters from the parameter grid are evaluated in serial. Parameter evaluation can be done in parallel by setting `parallelism` with a value of 2 or more (a value of 1 will be serial) before running model selection with `CrossValidator` or `TrainValidationSplit` (NOTE: this is not yet supported in Python).
+By default, sets of parameters from the parameter grid are evaluated in serial. Parameter evaluation can be done in parallel by setting `parallelism` with a value of 2 or more (a value of 1 will be serial) before running model selection with `CrossValidator` or `TrainValidationSplit`.
 The value of `parallelism` should be chosen carefully to maximize parallelism without exceeding cluster resources, and larger values may not always lead to improved performance.  Generally speaking, a value up to 10 should be sufficient for most clusters.
 
 # Cross-Validation