Precision and Recall are two important metrics used to evaluate the performance of classification models. Precision measures the accuracy of positive predictions, calculated as the number of true positive results divided by the total number of positive predictions. A high precision indicates that most predicted positives are indeed true positives. Recall, on the other hand, assesses the model's ability to identify all relevant instances. It is calculated as the number of true positives divided by the total number of actual positives. A high recall means that the model successfully captures most of the positive cases, minimizing false negatives.