R squared comparison I have 5 features in my data.
The R squared value when I use features 1,2, and 3 is $x$ and the R squared value when I use features 1,3, and 4 is $x + 0.1.$
Does this mean my second model is better than first model?

I would say you can't conclude that the second model is better just based on the R squared. For example, if we look at the cars dataset in R we can see that even by including a random variable with no relation to the dataset our R-Squared increases.
set.seed(1)
data("mtcars")
attach(mtcars)
two_pred <- lm(mpg~ disp + hp, data = mtcars)
summary(two_pred)$r.squared #0.748

random <- rnorm(nrow(mtcars))
two_pred_random <- lm(mpg~ disp + hp+ random, data = mtcars)
summary(two_pred_random)$r.squared #0.752

