A validation dataset 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 its architecture. By assessing how well the model performs on this separate dataset, developers can avoid overfitting, which occurs when a model learns the training data too well but fails to generalize to new, unseen data. Typically, the validation dataset is distinct from both the training dataset, which is used to train the model, and the test dataset, which is used for final evaluation. This separation ensures that the model's performance is assessed fairly and accurately, providing insights into its ability to make predictions on real-world data.