public class CrossValidation
Cross-validation is a technique for assessing how the results of a
statistical analysis will generalize to an independent data set.
It is mainly used in settings where the goal is prediction, and one
wants to estimate how accurately a predictive model will perform in
practice. One round of cross-validation involves partitioning a sample
of data into complementary subsets, performing the analysis on one subset
(called the training set), and validating the analysis on the other subset
(called the validation set or testing set). To reduce variability, multiple
rounds of cross-validation are performed using different partitions, and the
validation results are averaged over the rounds.