A validation set is a subset of data used to evaluate the performance of a machine learning model during training. It helps in tuning the model's parameters and making decisions about which model to select. By assessing the model on this separate dataset, developers can avoid overfitting, which occurs when a model performs well on training data but poorly on unseen data. Typically, the validation set is created by splitting the original dataset into three parts: the training set, the validation set, and the test set. The training set is used to train the model, while the validation set helps in fine-tuning. Finally, the test set is reserved for the final evaluation of the model's performance.