Cross-validation is a powerful technique to evaluate machine learning models before deployment. This post explains why hold-out validation may fail, introduces k-fold and leave-one-out cross-validation, and explores how stratified cross-validation handles imbalanced datasets—ensuring your models generalize well to unseen data.