This paper presents a model that uses a robot's verbal and nonverbal behaviors to successfully communicate object references to a human partner. This model, which is informed by computer vision, human-robot interaction, and cognitive psychology, simulates how low-level and high-level features of the scene might draw a user's attention. It then selects the most appropriate robot behavior that maximizes the likelihood that a user will understand the correct object reference while minimizing the cost of the behavior. We present a general computational framework for this model, then describe a specific implementation in a human-robot collaboration. Finally, we analyze the model's performance in two human evaluations—one video-based (75 participants) and one in person (20 participants)—and demonstrate that the system predicts the correct behaviors to perform successful object references.