The Adagrad algorithm is an adaptive learning rate optimization technique designed for training machine learning models, particularly in the context of online learning and stochastic optimization. It adjusts the learning rate for each parameter individually based on the historical gradients, allowing for more efficient convergence. By accumulating the squared gradients over time, Adagrad enables the model to take larger steps in directions with infrequent updates and smaller steps in directions with frequent updates. This characteristic makes it particularly useful for sparse data scenarios, where certain features may be more informative than others. Overall, Adagrad enhances the training process by improving stability and performance.