Model optimization is a crucial process in machine learning and data science that focuses on improving the performance of predictive models. It involves fine-tuning various parameters and hyperparameters to enhance the model’s accuracy and efficiency. The optimization process typically includes training the model on a dataset, evaluating its performance, and adjusting parameters such as learning rate, batch size, and the number of epochs. Techniques like gradient descent are commonly employed to minimize the error in predictions. Ultimately, effective model optimization leads to better generalization on unseen data, making the model more reliable in real-world applications.