What is Cross-Validation in Machine Learning?
Cross-Validation is a technique used to assess the performance and generalization ability of a
machine learning model. It involves dividing the available data into multiple subsets or folds and training the model on a subset while evaluating it on the remaining fold. Cross-validation helps estimate the model's performance on unseen data and mitigate issues such as
overfitting.
