The Adagrad algorithm is an adaptive learning rate optimization technique designed to improve the efficiency of gradient descent in machine learning. It adjusts the learning rate for each parameter individually based on the historical gradients, allowing for larger updates for infrequent parameters and smaller updates for frequent ones. This characteristic helps the algorithm converge faster, especially in scenarios with sparse data. Adagrad is particularly useful in online learning and stochastic optimization, where it can effectively handle varying data distributions. Its implementation is straightforward, making it a popular choice among practitioners in the field of deep learning and neural networks.