A test set is a collection of data used to evaluate the performance of a machine learning model after it has been trained. It helps determine how well the model can make predictions on new, unseen data. The test set is separate from the training set, which is used to teach the model. Using a test set is crucial for assessing the model's accuracy and generalization ability. By comparing the model's predictions against the actual outcomes in the test set, developers can identify any weaknesses and make necessary adjustments before deploying the model in real-world applications.