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 requiring extensive coding. Estimators can run on various platforms, including local hosts and distributed environments, and support different hardware configurations like CPUs, GPUs, and TPUs. They come in two forms: pre-made and custom. Pre-made Estimators encapsulate best practices, while custom Estimators allow for more flexibility but require users to implement their own model functions. However, it’s important to note that Estimators are not recommended for new code, as they are based on older TensorFlow paradigms.