Statistical Learning involves the development of algorithms and models that can learn from and make predictions based on data. It encompasses both supervised and unsupervised learning methods. Supervised learning uses labeled data to predict outcomes, while unsupervised learning explores patterns and structures in unlabeled data. Techniques such as regression, classification, clustering, and dimensionality reduction fall under this umbrella. The approach is grounded in statistical theory, providing a basis for measuring model performance and understanding data variability.