Cross-Validation is a method of evaluating and comparing predictive systems in statistics and machine learning.
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.
