Cross-Validation

A technique for evaluating a model's performance by training and testing it on different subsets of the data multiple times. The most common approach is k-fold cross-validation, where the data is split into k subsets and the model is trained k times, each time using a different subset as the test set. It gives a more reliable estimate of how well the model will generalize to new data than a single train-test split.
See also: overfitting, benchmark, Evaluation.