When there is an abundance of data available, a good way to train models is to split the available data into 3 parts: Train, Validation and Test.
We use the Train data to train the model, the Validation part is used to estimate the test error and select hyperparameters, and the Test is used to evaluate the performance of the model, and assess its generalization 
This is a common approach when training Artificial Neural Networks, and a good strategy to choose in data-rich environments. Therefore we should have some support of this data-analysis process in our Estimators.
 Friedman, Jerome, Trevor Hastie, and Robert Tibshirani. The elements of statistical learning. Vol. 1. Springer, Berlin: Springer series in statistics, 2001.