In supervised machine learning, *validating* is the process of fine-tuning and assessing a model's performance during training to ensure it generalizes well to unseen data. Unlike testing, validation occurs on a separate *validation set*, distinct from both training and testing data. The model uses the *features* of this set to make predictions, which are compared to the actual labels to calculate metrics like accuracy or loss. This helps monitor overfitting or underfitting and guides adjustments to model parameters or hyperparameters (e.g., learning rate or regularization). Validation ensures the classifier is optimized before its final evaluation on the test set.