Determining the degrees of freedom (DOF) of the linked rigid-body model, representing a multi-body motion of the human lower extremity, is one of the most important procedures in locomotion analysis. However, a trade-off exists between the quality of data fitting and the generalizability of the model. This study aimed to determine the optimal DOF of the model for the lower extremities that balance the goodness-of-fit and generalizability of the model during walking and running using Akaike's information criterion (AIC). Empirically obtained kinematic data for the lower extremities during walking and running were fitted by models with 9, 18, or 22 DOF. The relative quality of these models was assessed using their bias-corrected AIC (cAIC) value. A significant simple main effect of the model was found on the cAIC value for both walking and running conditions. Pairwise comparisons revealed that the cAIC value of the 18-DOF model was significantly smaller than that of the 9-DOF (walking: p < 0.001, running: p = 0.010) and 22-DOF (walking: p < 0.001, running: p < 0.001) models. These findings suggest that the 18-DOF model is optimal for representing the lower extremities during walking and running, in terms of goodness-of-fit and generalizability.