The study investigated the performance of several generic QT/RR regression models in a dataset of QT and RR intervals obtained from resting electrocardiograms of 1,100 healthy subjects (913 male, mean age 33+/-12 years). All the investigated models have three degrees of freedom and included the hyperparabolic and hyper-hyperbolic models, algorithmic models, negative exponential models, and models involving inverse tangent, hyperbolic tangent, and inverse hyperbolic sign functions. For each generic model, the combination of parameters leading to the lowest regression residuum was found. The results of the study show that the goodness of the optimum fit is practically independent of the generic form of the regression model and that different datasets lead to different combinations of the numerical values of parameters of the corresponding regression models. The study concludes that the search for a universally applicable QT/RR regression model that would provide the best fit in all circumstances is most likely fruitless. Rather, individual studies such as those investigating drug related QT prolongation might benefit from establishing a best-fit regression that would provide the optimum model for each particular dataset.