In general there is no way to choose the “best” model for a given problem without knowing something about the particular things you are investigating and the particular hypothesis you have.
In this case because you include X1 in all 3 models I assume that your “main” hypothesis is that X1 is related to Y. If that’s the case, then what you are worried about is that the relationship between X1 and Y might be confounded by some third variable that is correlated with both. But if you include that third variable in your model, then you have addressed this possibility. So in this example the best model is the one that accounts for the confounding variable or variables that you are most worried about. But you can’t figure that out in the abstract.
Let’s say you have a hypothesis that smoking (X1) might cause an increase in the likelihood of heart disease (Y). You should be worried that this relationship might be confounded by other variables that are independently correlated with BOTH Smoking and heart disease. But at the same time you don’t want to control for anything that MEDIATES the relationship between smoking and heart disease - i.e. any other intermediate factor that explains WHY smoking causes heart disease. Given all that, let’s say I showed you three models
Model 1: HeartDiseaseRisk=A1Smoking+B1UnhealthyEating+C1
Model 2: HeartDiseaseRisk=A1Smoking+B1HighBloodPressure+C1
Model 3: HeartDiseaseRisk=A1Smoking+B1GeneticPreDispostitionForHeartDisease+C1
There is no statistical test that can tell us which of these models is “best.” All three might have high R2 values and highly significant coefficients, but only the first one is actually useful in telling us whether smoking leads to heart disease, because it accounts for a potential confounder - we know that unhealthy eating can cause heart disease regardless of whether you smoke or not, and that people who smoke also tend to eat unhealthily. The other models are “bad,” either because they control for a mediator (high blood pressure) or because they control for a variable (genetic pre disposition for heart disease) that probably isn't strongly correlated with Smoking, instead of a variable that is (unhealthy eating). But we can only figure all this out by applying substantive knowledge of the sociological correlates of smoking, and the biological drivers of heart disease.