Model optimization is the process of improving a machine learning model's performance by adjusting its parameters and architecture to achieve the best possible results on a given dataset. It involves techniques such as hyperparameter tuning, regularization, and pruning to enhance accuracy, efficiency, and generalization capabilities of the model.