Spike train variability is of fundamental importance for understanding how information is encoded and processed in the nervous system. Most studies in this area have focused on short-term variability, as characterized by the coefficient of variation of the interspike interval distribution. Here we discuss the importance of extending the analysis of spike train variability to longer time scales that span multiple interspike intervals. Recent experimental and modeling studies of probability coding (P-type) electrosensory afferent nerve fibers in weakly electric fish have provided new insights into the functional importance of multiscale spike train variability. P-type afferent spike trains are moderately irregular on short time scales of a few milliseconds, but show significantly enhanced regularity on time scales of a few hundred milliseconds. This increased regularity is beyond what would be expected for a renewal process model in which successive intervals are uncorrelated. Modeling studies suggest that the correlation structure that underlies spike train regularization arises from relative refractory effects associated with a dynamic spike threshold. Spike train regularization in P-type afferents has been shown to significantly enhance signal detectability and information transmission on time scales that are functionally relevant for electrolocation behavior.