fixed wildcard import

Author: BryanCutler

docs/ml-tuning.md

@@ -56,7 +56,7 @@ 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.
 Sets of parameters from the parameter grid can be evaluated in parallel by setting `numParallelEval` with a value of 2 or more (a value of 1 will evaluate in serial) before running model selection with `CrossValidator` or `TrainValidationSplit`.
-The value of `numParallelEval` should be chosen carefully to maximize concurrency without exceeding cluster resources, and will be capped at the number of cores in the driver system.  Generally speaking, a value up to 10 should be sufficient for most clusters.
+The value of `numParallelEval` should be chosen carefully to maximize parallelism without exceeding cluster resources, and will be capped at the number of cores in the driver system.  Generally speaking, a value up to 10 should be sufficient for most clusters.
 
 
 # Cross-Validation

mllib/src/main/scala/org/apache/spark/ml/tuning/ValidatorParams.scala

@@ -27,7 +27,7 @@ import org.apache.spark.annotation.{Experimental, InterfaceStability}
 import org.apache.spark.SparkContext
 import org.apache.spark.ml.{Estimator, Model}
 import org.apache.spark.ml.evaluation.Evaluator
-import org.apache.spark.ml.param._
+import org.apache.spark.ml.param.{IntParam, Param, ParamMap, ParamPair, Params, ParamValidators}
 import org.apache.spark.ml.param.shared.HasSeed
 import org.apache.spark.ml.util._
 import org.apache.spark.ml.util.DefaultParamsReader.Metadata