Model calibration refers to the process of making adjustments or fine-tuning a model to ensure that its predictions or results align as closely as possible with the real world observations or data. It involves tweaking the model's parameters or inputs to improve its accuracy or match the expected outcomes. In simple terms, model calibration means adjusting a model to make it more accurate and reliable.