A test set is a collection of data used to evaluate the performance of a machine learning model. After a model is trained on a separate dataset called the training set, the test set helps determine how well the model can make predictions on new, unseen data. This is crucial for understanding the model's accuracy and reliability. Using a test set ensures that the model isn't just memorizing the training data but can generalize its learning to different situations. By comparing the model's predictions against the actual outcomes in the test set, developers can fine-tune the model and improve its effectiveness in real-world applications.