Cross-Validation

Definitions

1. A model evaluation technique that partitions data into complementary subsets, training on some folds and validating on others, then rotating. K-fold cross-validation (typically k=5 or 10) provides a more robust performance estimate than a single train/test split.

   In plain English: A way to test how well a machine learning model works by training and testing it on different slices of the data multiple times to get a reliable score.

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