Definition
Cross-Validation is a statistical method of evaluating
and comparing learning algorithms by dividing data
into two segments: one used to learn or train a model
and the other used to validate the model. In typical
cross-validation, the training and validation sets must
cross-over in successive rounds such that each data
point has a chance of being validated against. The basic
form of cross-validation is k-fold cross-validation.
Other forms of cross-validation are special cases of
k-fold cross-validation or involve repeated rounds
of k-fold cross-validation.