Model calibration is the process of adjusting model parameters to improve the agreement between model predictions and observed data, ensuring that the model's outputs are as close as possible to real-world values. It is crucial for enhancing the reliability and accuracy of predictive models, especially in fields like finance, weather forecasting, and machine learning.