Sequential sampling models have been tremendously successful in describing choice behavior and generating testable neural predictions. Missing to date is how these same mechanisms can flexibly give rise to the broad range of decisions humans make every day, including choosing the item they like most or least, or assigning a value to their option set as a whole. To test whether and how a single sequential sampling model could flexibly accommodate these and other types of decisions, we developed a theoretical framework that formalizes the necessary representations that align sequential sampling and evidence accumulation with one's current choice goals. We implement this framework within an extended leaky competing accumulator model and show that model simulations can parsimoniously explain behavior across a range of different choice goals, while also generating predictions for previously untested choice goals. Testing novel behavioral predictions of our model, we show that human behavior matches the predicted patterns, and further expand on previous work demonstrating that value-related patterns in gaze critically depend on the goals people have when engaging with options at hand.