# How does k-fold cross-validation reduce bias and variance?

_Last updated: 2026-07-20_  
_Reviewed by: Jason Burns, Editorial Steward_  
_Published by: AIDirectAnswers.com · Licensed under Citation License 1.0_

## Short answer

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.

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_Author: [Jason Burns](https://www.linkedin.com/in/jason-randolph-burns), Editorial Steward_  
_Published: 2026-07-20 · Modified: 2026-07-20_  
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