RMSProp, short for Root Mean Square Propagation, is an adaptive learning rate optimization algorithm used in training machine learning models. It adjusts the learning rate for each parameter based on the average of recent gradients, helping to stabilize the training process and improve convergence speed. This is particularly useful in scenarios with non-stationary objectives or when dealing with noisy data. The algorithm works by maintaining a moving average of the squared gradients, which allows it to adaptively scale the learning rate. By doing so, RMSProp can effectively handle the challenges posed by different feature scales and varying gradient magnitudes, making it a popular choice in deep learning frameworks like TensorFlow and PyTorch.