Ensemble learning involves a collection of machine learning methods aimed at improving the predictive performance of algorithms by combining many models. We will analyze the motivation behind using such methods to solve problems that arise from high bias and variance. Furthermore, we will present methods that allow the identification of bias and variance in machine learning models, as well as basic classes of ensemble learning methods.