ALS run method versus ALS train versus ALS fit and transform

[Carol McDonald]

In the Spark mllib examples MovieLensALS.scala  ALS run is used, however in
the movie recommendation with mllib tutorial ALS train is used , What is
the difference, when should you use one versus the other

    val model = new ALS()
      .setRank(params.rank)
      .setIterations(params.numIterations)
      .setLambda(params.lambda)
      .setImplicitPrefs(params.implicitPrefs)
      .setUserBlocks(params.numUserBlocks)
      .setProductBlocks(params.numProductBlocks)
      .run(training)


  val model = ALS.train(training, rank, numIter, lambda)

Also in org.apache.spark.examples.ml  , fit and transform is used. Which
one do you recommend using ?

    val als = new ALS()
      .setUserCol("userId")
      .setItemCol("movieId")
      .setRank(params.rank)
      .setMaxIter(params.maxIter)
      .setRegParam(params.regParam)
      .setNumBlocks(params.numBlocks)

    val model = als.fit(training.toDF())

    val predictions = model.transform(test.toDF()).cache()