Support vectors are the data points that lie closest to the decision boundary in a Support Vector Machine (SVM) model, and they are critical in defining the position and orientation of the hyperplane. These vectors are the only elements necessary to determine the optimal hyperplane, making them essential for the generalization capability of the SVM model.