A validation set is a subset of data separate from the training set, used to assess the performance and generalization ability of a machine learning model during the training process. It provides a means to tune hyperparameters and make decisions about model architecture without biasing the model toward the test set. By evaluating the model on the validation set, practitioners can monitor metrics such as accuracy, precision, recall, and loss to determine if the model is overfitting or underfitting. The validation set helps ensure that the model can effectively generalize to unseen data, ultimately improving its performance when deployed in real-world applications.