Gated Recurrent Units (GRUs) are a type of recurrent neural network architecture designed to handle sequence data, offering a simplified alternative to Long Short-Term Memory (LSTM) networks by using fewer gates. They are particularly effective in capturing dependencies in time series data while being computationally more efficient due to their reduced complexity compared to LSTMs.