Cross-Validation Techniques for ML Models (2026 Guide)
Updated on January 30, 2026 4 minutes read
Frequently Asked Questions
What is the difference between cross-validation and a train-test split?
A train-test split evaluates your model once on a single holdout set. Cross-validation repeats training and validation across multiple folds and averages results, giving a more stable estimate especially with limited data.
When should I use stratified k-fold cross-validation?
Use stratified k-fold for classification tasks where classes are imbalanced. It helps keep class proportions similar across folds so your scores are less sensitive to unlucky splits.
Do I still need a test set if I use cross-validation?
If you can afford it, yes. Cross-validation helps with model selection and tuning on training data, while a final untouched test set provides an extra check before you report results or deploy.