Precision measures the proportion of true positive predictions among all positive predictions made by the model, focusing on the accuracy of the positive predictions. Recall, on the other hand, measures the proportion of true positives among all actual positives, focusing on the model's ability to detect positive instances. The choice between optimizing for precision or recall depends on the specific costs associated with false positives and false negatives in the problem context.