How can I compare a NN model and a linear regression?

I have a small dataset (1500 rows) and to predict the imbalanced target, I am running two linear models (linear regression and lasso) and one nonlinear model (Neural Network) on it. I am using Area Under Precision Recall Curve (AUPRC) to compare the three models. The baseline in the curve is 10%, AUC for linear regression is 11%, AUC for lasso is 11.2%, and AUC for NN is 11.35%. Can I say that the learning models have improved the random guessing? Is the difference between lasso and NN (11.2% and 11.35%) enough to say the relationships are nonlinear rather than linear? Is there any way (test) to show that the scores are significantly different (such as t-test or p-values)?