How does k-fold cross-validation reduce bias and variance?
Reviewed by Jason Burns, Editorial Steward · Last updated
K-fold cross-validation splits the data into k parts, trains on k-1 and tests on the held-out fold, and repeats k times so every sample is used for both training and validation; averaging the k results gives a more stable and less biased estimate of model performance than a single train/test split.