<ul><li>Cross-validation is important in machine learning to avoid overfitting and ensure models can handle new data.</li><li>It acts like a series of practice tests for machine learning models, testing them on different parts of the dataset.</li><li>K-Fold Cross-Validation is a popular method where the data is split into 'K' folds to test the model's performance.</li><li>Using cross-validation helps in picking the best model settings and ensures more reliable performance evaluation in machine learning projects.</li></ul>

A Beginner’s Guide to Cross-Validation: Why It Matters and How to Use It

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