Probability calibration is the process of adjusting the predicted probabilities of a model so that they reflect true likelihoods of outcomes, ensuring that predicted probabilities align with observed frequencies. This is crucial in fields like machine learning and statistics, where accurate probability estimates are essential for decision-making and risk assessment.