Neural Architecture Search (NAS) is a technique in machine learning that automates the design of neural networks. Instead of manually creating architectures, NAS uses algorithms to explore various configurations and identify the most effective ones for a specific task. This process can significantly improve performance and reduce the time required for model development. NAS typically involves two main components: a search space, which defines the possible architectures, and a search strategy, which determines how to explore that space. By leveraging methods like reinforcement learning or evolutionary algorithms, NAS can efficiently discover optimal network designs tailored to particular datasets or applications.