Estimators are a high-level abstraction in TensorFlow that simplify the process of building machine learning models. They provide a framework for training, evaluating, and making predictions without the need to manage the underlying computational graph directly. Estimators can run on various hardware configurations, including CPUs, GPUs, and TPUs, and support distributed training across multiple servers. They come in two forms: pre-made Estimators, which encapsulate best practices, and custom Estimators, where users define their model functions. However, it’s important to note that Estimators are not recommended for new code, as TensorFlow’s Keras API offers a more user-friendly approach for model development.