Model calibration is the systematic process of adjusting the parameters of a computational or mathematical model to minimize the discrepancy between its outputs and empirical observations or established data about the system being modeled. This methodological approach investigates the sensitivity of model behavior to parameter variations, employing statistical and optimization techniques to identify values that enhance model validity, reliability, and predictive accuracy for representing complex phenomena.