Data is a critical resource for Machine Learning (ML), yet working with data remains a key friction point. This paper introduces Croissant, a metadata format for datasets that creates a shared representation across ML tools, frameworks, and platforms.Croissant makes datasets more discoverable, portable, and interoperable, thereby addressing significant challenges in ML data management and responsible AI. Croissant is already supported by several popular dataset repositories, spanning hundreds of thousands of datasets, enabling easy loading without changes into the most commonly-used ML frameworks, regardless of where the data is stored. Our initial evaluation shows that Croissant metadata is deemed readable, understandable, complete, yet concise by human raters.