Model Optimization is a critical phase in machine learning that involves fine-tuning models to improve their accuracy, efficiency, and performance. This page delves into various optimization techniques such as hyperparameter tuning, regularization, and cross-validation. Discover how these methods not only enhance model performance but also prevent issues like overfitting and underfitting, ensuring that machine learning models are robust, reliable, and ready for real-world applications.