[SPARK-30933][ML][DOCS] ML, GraphX 3.0 QA: Update user guide for new …

…features & APIs

### What changes were proposed in this pull request?
Change ml-tuning.html.

### Why are the changes needed?
Add description for ```MultilabelClassificationEvaluator``` and ```RankingEvaluator```.

### Does this PR introduce any user-facing change?
Yes

before:
![image](https://user-images.githubusercontent.com/13592258/76437013-2c5ffb80-6376-11ea-8946-f5c2e7379b7c.png)

after:
![image](https://user-images.githubusercontent.com/13592258/76437054-397cea80-6376-11ea-867f-fe8d8fa4e5b3.png)

### How was this patch tested?

Closes apache#27880 from huaxingao/spark-30933.

Authored-by: Huaxin Gao <[email protected]>
Signed-off-by: Sean Owen <[email protected]>

docs/ml-tuning.md

@@ -65,9 +65,11 @@ At a high level, these model selection tools work as follows:
 
 The `Evaluator` can be a [`RegressionEvaluator`](api/scala/org/apache/spark/ml/evaluation/RegressionEvaluator.html)
 for regression problems, a [`BinaryClassificationEvaluator`](api/scala/org/apache/spark/ml/evaluation/BinaryClassificationEvaluator.html)
-for binary data, or a [`MulticlassClassificationEvaluator`](api/scala/org/apache/spark/ml/evaluation/MulticlassClassificationEvaluator.html)
-for multiclass problems. The default metric used to choose the best `ParamMap` can be overridden by the `setMetricName`
-method in each of these evaluators.
+for binary data, a [`MulticlassClassificationEvaluator`](api/scala/org/apache/spark/ml/evaluation/MulticlassClassificationEvaluator.html)
+for multiclass problems, a [`MultilabelClassificationEvaluator`](api/scala/org/apache/spark/ml/evaluation/MultilabelClassificationEvaluator.html)
+ for multi-label classifications, or a
+[`RankingEvaluator`](api/scala/org/apache/spark/ml/evaluation/RankingEvaluator.html) for ranking problems. The default metric used to
+choose the best `ParamMap` can be overridden by the `setMetricName` method in each of these evaluators.
 
 To help construct the parameter grid, users can use the [`ParamGridBuilder`](api/scala/org/apache/spark/ml/tuning/ParamGridBuilder.html) 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`.