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This survey provides a comprehensive overview of the landscape of crowdsourcing research, targeted at the machine learning community. We begin with an overview of the ways in which crowdsourcing can be used to advance machine learning research, focusing on four application areas: 1) data generation, 2) evaluation and debugging of models, 3) hybrid intelligence systems that leverage the complementary strengths of humans and machines to expand the capabilities of AI, and 4) crowdsourced behavioral experiments that improve our understanding of how humans interact with machine learning systems and technology more broadly. We next review the extensive literature on the behavior of crowdworkers themselves. This research, which explores the prevalence of dishonesty among crowdworkers, how workers respond to both monetary incentives and intrinsic forms of motivation, and how crowdworkers interact with each other, has immediate implications that we distill into best practices that researchers should follow when using crowdsourcing in their own research. We conclude with a discussion of additional tips and best practices that are crucial to the success of any project that uses crowdsourcing, but rarely mentioned in the literature.