Model evaluation is the process of assessing how well a machine learning model performs on a given task. It involves using various metrics to measure the model's accuracy, precision, recall, and other relevant factors. By comparing the model's predictions against actual outcomes, we can determine its effectiveness and identify areas for improvement. To evaluate a model, we often split our data into training and testing sets. The model learns from the training set, while the testing set helps us gauge its performance on unseen data. This approach ensures that the model generalizes well and is not just memorizing the training data, leading to better results in real-world applications.