Statistical learning theory is a framework that combines statistics and machine learning to understand how algorithms can learn from data. It focuses on the principles that govern the performance of learning models, helping to determine how well they can generalize from training data to unseen data. The theory provides tools to analyze the trade-off between model complexity and accuracy, guiding the selection of appropriate models for specific tasks. By using concepts like overfitting and underfitting, it helps researchers and practitioners improve their models and make better predictions based on available data.