Testing a model's predictive accuracy on data separate from that used to build it.
Cross-validation checks whether a statistical model's predictions hold up in an independent sample or subset not used to derive the model, guarding against overfitting to the original dataset. Its absence, as noted here, means a model's apparent predictive strength may not generalise beyond the sample it was built on.
This guide was auto-drafted and is pending editorial review.