Due to the rapid growth of smart grid applications all over the globe, it has become a more attractive target to malicious actors. Countries and stakeholders (e.g., governments) spend billions of dollars on ensuring the continuity and security of their smart grids for strategic and operational reasons. In fact, the risk associated with compromising a smart grid is considered among the highest in the cybersecurity world. This paper surveys a group of well-known smart grid intrusion detection datasets that are used in the development of machine learning-based intrusion detection systems. The study presents an analysis of these datasets and provides recommendations for researchers utilizing them.