Transformers are a type of neural network architecture that excels in processing sequential data by leveraging self-attention mechanisms, enabling them to capture long-range dependencies more effectively than previous models like RNNs. They have become the foundation for many state-of-the-art models in natural language processing, including BERT and GPT, due to their scalability and ability to handle large datasets.