A Gated Recurrent Unit (GRU) is a type of recurrent neural network architecture that is designed to handle sequential data by using gating units to control the flow of information, improving the capture of dependencies in sequences. It is similar to the Long Short-Term Memory (LSTM) network but with a simpler structure, making it computationally more efficient while still effectively addressing the vanishing gradient problem.