Statistical Learning Theory is a framework that combines statistics and machine learning to understand how algorithms can learn from data. It focuses on how to make predictions or decisions based on observed data while managing uncertainty. This theory helps in evaluating the performance of different learning models, guiding researchers and practitioners in choosing the best approach for their specific problems. At its core, Statistical Learning Theory provides tools to analyze the trade-off between model complexity and accuracy. Concepts like overfitting and generalization are central to this theory, helping to ensure that models not only perform well on training data but also on new, unseen data.