Cognitive control, the ability to flexibly and selectively process information in the service of higher-level goals, is essential to daily functioning. However, despite the burgeoning research in this domain, much remains to be understood regarding its underlying neurocomputational mechanisms. This chapter highlights several prominent models that have made significant progress towards understanding the core principles of neural information processing and computation that are central to cognitive control. Neural network models are reviewed that characterize: (1) how tasks are represented, updated, and learned (e.g., attentional control, task-switching, structure learning); and (2) how cognitive control is evaluated and allocated based on assessments of demand (e.g., conflict monitoring, outcome prediction, and expected value of control). This brief survey of influential theoretical models provides an important foundational introduction into the primary mechanisms of cognitive control, and concludes with key open questions and future directions aimed at developing a fuller understanding of this domain.