The AdaGrad algorithm, short for Adaptive Gradient Algorithm, is an optimization technique designed to enhance the performance of gradient descent methods. It adapts the learning rate for each parameter based on the historical gradients, allowing for larger updates for infrequently updated parameters and smaller updates for frequently updated ones. This adaptive approach helps in efficiently navigating the search space, particularly in scenarios with sparse data or varying feature scales. By automatically adjusting the learning rates, AdaGrad accelerates convergence and improves the overall optimization process, making it a popular choice in machine learning and deep learning applications.