Unless 'user_id' has some information more than just being a unique identifier for a specific person...then you are creating a couple of issues in your model. If there is other information to be gathered (other than just a unique number) then it is best to try to capture that information in some other way. This could happen because your user_id is actually correlated to another variable and is acting as a stand-in for that variable.
For example: if user_id is actually representing how long the user joined the system -- then you translate the number to an ordinal or approximate time variable. However, you have to determine whether there are issues with with using user_id as a substitute for time. Did only 10 people join the first year and 100 the second and 1000 the third? Generally it is better to see if there is a sign-up date elsewhere in the data. In the time example you may be able to simply replace everyone who doesn't have an account with the shortest amount of time possible because they don't have an account.
Otherwise, if the variable can't be translated into something more meaningful:
1) You are over-fitting by using a variable that only applies to individuals in order to find a result. Example: It's great to know that Bob is more likely to make a claim than Judy is...but it has nothing to do with a model predicting whether John will. Your model will be tweaked to intentionally be better at the training set (even more than it normally would be).
2) You may not be capturing the actual information that is impacting your results. Does any individual user_id in the data occur more than once? If it doesn't occur more than once, and is having a meaningful effect on your model, then it is likely that the variable correlates to something else that is really having the impact. Try identifying what the correlation might be. If user_id does occur more than once then some models will simply weight the user_id based on how many times it occurs which is a frequency and not related the the actual user_id itself (and you can transform the data to represent this). Determine what the impact is of having multiple instances of a user id and how to deal with that.
It sounds like there is some data exploration still needed unless user_id means something different in your data set than it normally does.