Cost-sensitive learning is a type of machine learning that takes into account the varying costs of different types of errors, aiming to minimize the overall cost rather than just the error rate. This approach is particularly useful in situations where false positives and false negatives have significantly different consequences, such as in medical diagnosis or fraud detection.