A test dataset is a collection of data used to evaluate the performance of a machine learning model. It is separate from the training dataset, which is used to teach the model. The test dataset helps determine how well the model can make predictions on new, unseen data. Using a test dataset is crucial for assessing the model's accuracy and generalization ability. By comparing the model's predictions against the actual outcomes in the test dataset, developers can identify areas for improvement and ensure that the model performs reliably in real-world applications.