Using survival analysis models with uncensored data for time-to-event prediction

Are there any advantages of using survival analysis models like Cox’s proportional hazard model with uncensored data over simple linear regression or other classic ML models? I have data with recurrent events and I try to predict the time of the next event. Data contains about 2000 different subjects and about 60 events per subject. The percentage of censored data (the last event of each subject) is small, and I don't think it plays a big role in the prediction.